Data Preprocessing Frontal lobe

Load and annotate data

# Load csvs
datExpr = read.csv('./../Data/Gandal/RNAseq_ASD_datExpr.csv', row.names=1)
datMeta = read.csv('./../Data/Gandal/RNAseq_ASD_datMeta.csv')

# Group brain regions by lobes
datMeta$Brain_Region = as.factor(datMeta$Region)
datMeta$Brain_lobe = 'Occipital'
datMeta$Brain_lobe[datMeta$Brain_Region %in% c('BA4_6', 'BA9', 'BA24', 'BA44_45')] = 'Frontal'
datMeta$Brain_lobe[datMeta$Brain_Region %in% c('BA3_1_2_5', 'BA7')] = 'Parietal'
datMeta$Brain_lobe[datMeta$Brain_Region %in% c('BA38', 'BA39_40', 'BA20_37', 'BA41_42_22')] = 'Temporal'
datMeta$Brain_lobe=factor(datMeta$Brain_lobe, levels=c('Frontal', 'Temporal', 'Parietal', 'Occipital'))

# Remove '/' from Batch variable: (It is recommended (but not required) to use only letters, numbers, 
# and delimiters '_' or '.', in levels of factors as these are safe characters for column names in R
datMeta$Batch = gsub('/', '.', datMeta$RNAExtractionBatch) %>% as.factor

# Transform Diagnosis into a factor variable
datMeta$Diagnosis_ = factor(datMeta$Diagnosis_, levels=c('CTL','ASD'))

Filter Frontal lobe samples

datExpr = datExpr %>% dplyr::select(paste0('X',datMeta$Dissected_Sample_ID[datMeta$Brain_lobe=='Frontal']))
datMeta = datMeta %>% filter(Brain_lobe=='Frontal')

Check sample composition

Data description taken from the dataset’s synapse entry: RNAseq data was generated from 88 postmortem cortex brain samples from subjects with ASD (53 samples from 24 subjects) and non-psychiatric controls (35 samples from 17 subjects), across four cortical regions encompassing all major cortical lobes – frontal, temporal, parietal, and occipital. Brain samples were obtained from the Harvard Brain Bank as part of the Autism Tissue Project (ATP).

print(paste0('Dataset includes ', nrow(datExpr), ' genes from ', ncol(datExpr), ' samples belonging to ', length(unique(datMeta$Subject_ID)), ' different subjects.'))

## [1] "Dataset includes 63682 genes from 22 samples belonging to 22 different subjects."

Diagnosis distribution: There are more ASD samples than controls

table(datMeta$Diagnosis_)

## 
## CTL ASD 
##  13   9

Sex distribution: There are only 3 Female samples

table(datMeta$Sex)

## 
##  F  M 
##  3 19

Age distribution: Subjects between 2 and 56 years old with a mean close to 30

summary(datMeta$Age)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    2.00   19.25   25.50   27.55   36.50   56.00

Annotate genes with BioMart information

Processing group

All the information we have is the Brain Bank, and although all the samples were obtained from the Autism Tissue Project, we don’t have any more specific information about who preprocessed each sample

table(datMeta$Brain_Bank)

## 
## ATP 
##  20

Date of processing

There are two different dates when the data was procesed. Most samples belong to the October batch

table(datMeta$RNAExtractionBatch)

## 
## 10/10/2014  6/20/2014 
##         14          6

All but one of the autism samples come from the October batch. This could be a problem when performing supervised batch correction (comBat) because by removing batch effect we could be removing also the biological signal related to diagnosis.

table(datMeta$RNAExtractionBatch, datMeta$Diagnosis_)

##             
##              CTL ASD
##   10/10/2014   8   6
##   6/20/2014    5   1

Samples don’t seem to cluster together that strongly for each batch, although there does seem to be some kind of relation

There doesn’t seem to be a strong relation with the other variables, including diagnosis

h_clusts = datExpr %>% t %>% dist %>% hclust %>% as.dendrogram

create_viridis_dict = function(){
  min_age = datMeta$Age %>% min
  max_age = datMeta$Age %>% max
  viridis_age_cols = viridis(max_age - min_age + 1)
  names(viridis_age_cols) = seq(min_age, max_age)
  
  return(viridis_age_cols)
}
viridis_age_cols = create_viridis_dict()

dend_meta = datMeta[match(substring(labels(h_clusts),2), datMeta$Dissected_Sample_ID),] %>% 
            mutate('Batch' = ifelse(RNAExtractionBatch=='10/10/2014', '#F8766D', '#00BFC4'),
                   'Diagnosis' = ifelse(Diagnosis_=='CTL','#008080','#86b300'), # Blue control, Green ASD
                   'Sex' = ifelse(Sex=='F','#ff6666','#008ae6'),                # Pink Female, Blue Male
                   'Age' = viridis_age_cols[as.character(Age)]) %>%             # Purple: young, Yellow: old
            dplyr::select(Age, Sex, Diagnosis, Batch)
h_clusts %>% set('labels', rep('', nrow(datMeta))) %>% set('branches_k_color', k=9) %>% plot
colored_bars(colors=dend_meta)

rm(h_clusts, dend_meta, create_viridis_dict, viridis_age_cols)

There seems to be a different behaviour by batch mainly in the second and third principal components, although this behaviour could be related to diagnosis

pca = datExpr %>% t %>% prcomp
summary(pca)$importance[,1:3]

##                                 PC1         PC2         PC3
## Standard deviation     202055.45733 91840.41339 68156.44883
## Proportion of Variance      0.64481     0.13322     0.07337
## Cumulative Proportion       0.64481     0.77803     0.85139

plot_data = data.frame('ID'=colnames(datExpr), 'PC1' = pca$x[,1], 'PC2' = pca$x[,2], 'PC3' = pca$x[,3]) %>% 
            mutate('ID'=substring(ID,2)) %>% left_join(datMeta, by=c('ID'='Dissected_Sample_ID')) %>% 
            mutate('Batch'=RNAExtractionBatch) %>% dplyr::select('PC1','PC2','PC3','Batch')

plot_data %>% ggpairs(progress=FALSE, aes(colour=Batch, fill=Batch, alpha=0.3)) + theme_minimal()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

rm(pca, plot_data)

Comparing the mean expression of each sample by batch we can see the batch effect is reflected here as well, although, again, this could be related to diagnosis

plot_data_b1 = data.frame('Mean'=colMeans(datExpr[,datMeta$RNAExtractionBatch=='10/10/2014']), 'Batch'='10/10/2014')
plot_data_b2 = data.frame('Mean'=colMeans(datExpr[,datMeta$RNAExtractionBatch=='6/20/2014']), 'Batch'='6/20/2014')

plot_data = rbind(plot_data_b1, plot_data_b2)
mu = plot_data %>% group_by(Batch) %>% dplyr::summarise(BatchMean=mean(Mean))

ggplotly(plot_data %>% ggplot(aes(x=Mean, color=Batch, fill=Batch)) + geom_density(alpha=0.3) + 
         geom_vline(data=mu, aes(xintercept=BatchMean, color=Batch), linetype='dashed') +
         ggtitle('Mean expression by sample grouped by Batch') + scale_x_log10() + theme_minimal())

rm(plot_data_b1, plot_data_b2, plot_data, mu)

Looking for unknown sources of batch effects

Following the pipeline from Surrogate variable analysis: hidden batch effects where sva is used with DESeq2.

Create a DeseqDataSet object, estimate the library size correction and save the normalized counts matrix

counts = datExpr %>% as.matrix
rowRanges = GRanges(datGenes$chromosome_name,
                  IRanges(datGenes$start_position, width=datGenes$length),
                  strand=datGenes$strand,
                  feature_id=datGenes$ensembl_gene_id)
se = SummarizedExperiment(assays=SimpleList(counts=counts), rowRanges=rowRanges, colData=datMeta)
dds = DESeqDataSet(se, design = ~ Diagnosis_)

dds = estimateSizeFactors(dds)
norm.cts = counts(dds, normalized=TRUE)

Provide the normalized counts and two model matrices to SVA. The first matrix uses the biological condition, and the second model matrix is the null model.

mod = model.matrix(~ Diagnosis_, colData(dds))
mod0 = model.matrix(~ 1, colData(dds))
sva_fit = svaseq(norm.cts, mod=mod, mod0=mod0)

## Number of significant surrogate variables is:  4 
## Iteration (out of 5 ):1  2  3  4  5

rm(mod, mod0)

Found 4 surrogate variables, since there is no direct way to select which ones to pick Bioconductor answer, kept all of them.

Include SV estimations to datMeta information

sv_data = sva_fit$sv %>% data.frame
colnames(sv_data) = paste0('SV',1:ncol(sv_data))

datMeta_sva = cbind(datMeta, sv_data)

rm(sv_data)

In conclusion: Date of extraction could work as a surrogate for batch effect but we cannot use it because it is correlated to the diagnosis. The sva package found other 4 variables that could work as surrogates which are now included in datMeta and should be included in the DEA.

Save filtered and annotated dataset

save(datExpr, datMeta, datGenes, file='./../Data/Gandal/filtered_raw_data_frontal.RData')
#load('./../Data/Gandal/filtered_raw_data_frontal.RData')

datExpr = datExpr_vst
datMeta = datMeta_vst %>% data.frame
datGenes = datGenes_vst

print(paste0('After filtering, the dataset consists of ', nrow(datExpr), ' genes and ', ncol(datExpr), ' samples'))

## [1] "After filtering, the dataset consists of 26565 genes and 20 samples"

rm(datExpr_vst, datMeta_vst, datGenes_vst)

SVA surrogate variables

In some places they say you shouldn’t correct these effects on the data because you risk losing biological variation, in others they say you should because they introduce noise to the data. The only thing everyone agrees on is that you shouldn’t remove them before performing DEA but instead include them in the model.

Based on the conclusions from Practical impacts of genomic data “cleaning” on biological discovery using surrogate variable analysis it seems like it may be a good idea to remove the batch effects from the data and not only from the DE analysis:

• Using SVA, ComBat or related tools can increase the power to identify specific signals in complex genomic datasets (they found “greatly sharpened global and gene-specific differential expression across treatment groups”)

• But caution should be exercised to avoid removing biological signal of interest

• We must be precise and deliberate in the design and analysis of experiments and the resulting data, and also mindful of the limitations we impose with our own perspective

• Open data exploration is not possible after such supervised “cleaning”, because effects beyond those stipulated by the researcher may have been removed

# Taken from https://www.biostars.org/p/121489/#121500
correctDatExpr = function(datExpr, mod, svs) {
  X = cbind(mod, svs)
  Hat = solve(t(X) %*% X) %*% t(X)
  beta = (Hat %*% t(datExpr))
  rm(Hat)
  gc()
  P = ncol(mod)
  return(datExpr - t(as.matrix(X[,-c(1:P)]) %*% beta[-c(1:P),]))
}

pca_samples_before = datExpr %>% t %>% prcomp
pca_genes_before = datExpr %>% prcomp

# Correct
mod = model.matrix(~ Diagnosis_, colData(dds))
svs = datMeta %>% dplyr::select(SV1:SV4) %>% as.matrix
datExpr_corrected = correctDatExpr(as.matrix(datExpr), mod, svs)

pca_samples_after = datExpr_corrected %>% t %>% prcomp
pca_genes_after = datExpr_corrected %>% prcomp

pca_samples_df = rbind(data.frame('ID'=colnames(datExpr), 'PC1'=pca_samples_before$x[,1],
                                  'PC2'=pca_samples_before$x[,2], 'corrected'=0),
                       data.frame('ID'=colnames(datExpr), 'PC1'=pca_samples_after$x[,1],
                                  'PC2'=pca_samples_after$x[,2], 'corrected'=1)) %>%
                 left_join(datMeta %>% mutate('ID'=rownames(datMeta)), by='ID')

ggplotly(pca_samples_df %>% ggplot(aes(PC1, PC2, color=Diagnosis_)) + geom_point(aes(frame=corrected, id=ID)) + 
         xlab(paste0('PC1 (corr=', round(cor(pca_samples_before$x[,1],pca_samples_after$x[,1]),2),
                     '). % Var explained: ', round(100*summary(pca_samples_before)$importance[2,1],1),' to ',
                     round(100*summary(pca_samples_after)$importance[2,1],1))) +
         ylab(paste0('PC2 (corr=', round(cor(pca_samples_before$x[,2],pca_samples_after$x[,2]),2),
                     '). % Var explained: ', round(100*summary(pca_samples_before)$importance[2,2],1),' to ',
                     round(100*summary(pca_samples_after)$importance[2,2],1))) +
         ggtitle('Samples') + theme_minimal())

rm(pca_samples_df)

pca_genes_df = rbind(data.frame('ID'=rownames(datExpr), 'PC1'=pca_genes_before$x[,1],
                                'PC2'=pca_genes_before$x[,2], 'corrected'=0, 'MeanExpr'=rowMeans(datExpr)),
                     data.frame('ID'=rownames(datExpr), 'PC1'=pca_genes_after$x[,1],
                                'PC2'=pca_genes_after$x[,2], 'corrected'=1, 'MeanExpr'=rowMeans(datExpr)))

keep_genes = rownames(datExpr) %>% sample(0.1*nrow(datExpr))

pca_genes_df = pca_genes_df %>% filter(ID %in% keep_genes)

ggplotly(pca_genes_df %>% ggplot(aes(PC1, PC2,color=MeanExpr)) + geom_point(alpha=0.3, aes(frame=corrected, id=ID)) +
         xlab(paste0('PC1 (corr=', round(cor(pca_genes_before$x[,1],pca_genes_after$x[,1]),2),
                     '). % Var explained: ', round(100*summary(pca_genes_before)$importance[2,1],1),' to ',
                     round(100*summary(pca_genes_after)$importance[2,1],1))) +
         ylab(paste0('PC2 (corr=', round(cor(pca_genes_before$x[,2],pca_genes_after$x[,2]),2),
                     '). % Var explained: ', round(100*summary(pca_genes_before)$importance[2,2],1),' to ',
                     round(100*summary(pca_genes_after)$importance[2,2],1))) +
         scale_color_viridis() + ggtitle('Genes') + theme_minimal())

rm(pca_samples_before, pca_genes_before, mod, svs, pca_samples_after, pca_genes_after, pca_samples_df, keep_genes)

datExpr = datExpr_corrected

Processing date

There seems to still be a difference in the behaviour in the second and third PC to the processing date, although not as big as before

pca = datExpr %>% t %>% prcomp
summary(pca)$importance[,1:3]

##                             PC1      PC2      PC3
## Standard deviation     13.89120 13.22384 11.55192
## Proportion of Variance  0.13021  0.11800  0.09005
## Cumulative Proportion   0.13021  0.24821  0.33826

plot_data = data.frame('ID'=colnames(datExpr), 'PC1' = pca$x[,1], 'PC2' = pca$x[,2], 'PC3' = pca$x[,3]) %>% 
            mutate('ID'=substring(ID,2)) %>% left_join(datMeta, by=c('ID'='Dissected_Sample_ID')) %>% 
            mutate('Batch'=RNAExtractionBatch) %>% dplyr::select('PC1','PC2','PC3','Batch')

plot_data %>% ggpairs(progress=FALSE, aes(colour=Batch, fill=Batch, alpha=0.3)) + theme_minimal()

rm(pca, plot_data)

Even after correcting the dataset for the surrogate variables found with sva, there is still a difference in mean expression by processing date

plot_data_b1 = data.frame('Mean'=colMeans(datExpr[,datMeta$RNAExtractionBatch=='10/10/2014']), 'Batch'='10/10/2014')
plot_data_b2 = data.frame('Mean'=colMeans(datExpr[,datMeta$RNAExtractionBatch=='6/20/2014']), 'Batch'='6/20/2014')

plot_data = rbind(plot_data_b1, plot_data_b2)
mu = plot_data %>% group_by(Batch) %>% dplyr::summarise(BatchMean=mean(Mean))

ggplotly(plot_data %>% ggplot(aes(x=Mean, color=Batch, fill=Batch)) + geom_density(alpha=0.3) + 
         geom_vline(data=mu, aes(xintercept=BatchMean, color=Batch), linetype='dashed') +
         ggtitle('Mean expression by sample grouped by processing date') + scale_x_log10() + theme_minimal())

rm(plot_data_b1, plot_data_b2, plot_data, mu)

Performing Batch Correction for processing date

Since there is a correlation between diagnosis and processing date, I won’t correct for this batch effect because I may lose important biological signals related to autism

save(datExpr, datMeta, datGenes, DE_info, dds, file='./../Data/Gandal/preprocessed_data_frontal.RData')

sessionInfo()

## R version 3.5.2 (2018-12-20)
## Platform: x86_64-redhat-linux-gnu (64-bit)
## Running under: Scientific Linux 7.6 (Nitrogen)
## 
## Matrix products: default
## BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so
## 
## locale:
##  [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8    
##  [5] LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8   
##  [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] hexbin_1.27.2               dendextend_1.10.0          
##  [3] vsn_3.50.0                  WGCNA_1.66                 
##  [5] fastcluster_1.1.25          dynamicTreeCut_1.63-1      
##  [7] sva_3.30.1                  genefilter_1.64.0          
##  [9] mgcv_1.8-26                 nlme_3.1-137               
## [11] DESeq2_1.22.2               SummarizedExperiment_1.12.0
## [13] DelayedArray_0.8.0          BiocParallel_1.16.6        
## [15] matrixStats_0.54.0          Biobase_2.42.0             
## [17] GenomicRanges_1.34.0        GenomeInfoDb_1.18.2        
## [19] IRanges_2.16.0              S4Vectors_0.20.1           
## [21] BiocGenerics_0.28.0         biomaRt_2.38.0             
## [23] ggExtra_0.8                 GGally_1.4.0               
## [25] gridExtra_2.3               viridis_0.5.1              
## [27] viridisLite_0.3.0           RColorBrewer_1.1-2         
## [29] plotlyutils_0.0.0.9000      plotly_4.8.0               
## [31] glue_1.3.1                  reshape2_1.4.3             
## [33] forcats_0.3.0               stringr_1.4.0              
## [35] dplyr_0.8.0.1               purrr_0.3.1                
## [37] readr_1.3.1                 tidyr_0.8.3                
## [39] tibble_2.1.1                ggplot2_3.1.0              
## [41] tidyverse_1.2.1            
## 
## loaded via a namespace (and not attached):
##   [1] readxl_1.1.0           backports_1.1.2        Hmisc_4.1-1           
##   [4] plyr_1.8.4             lazyeval_0.2.2         splines_3.5.2         
##   [7] crosstalk_1.0.0        robust_0.4-18          digest_0.6.18         
##  [10] foreach_1.4.4          htmltools_0.3.6        GO.db_3.7.0           
##  [13] magrittr_1.5           checkmate_1.8.5        memoise_1.1.0         
##  [16] fit.models_0.5-14      cluster_2.0.7-1        doParallel_1.0.14     
##  [19] limma_3.38.3           annotate_1.60.1        modelr_0.1.4          
##  [22] prettyunits_1.0.2      colorspace_1.4-1       blob_1.1.1            
##  [25] rvest_0.3.2            rrcov_1.4-3            haven_1.1.1           
##  [28] xfun_0.5               crayon_1.3.4           RCurl_1.95-4.10       
##  [31] jsonlite_1.6           impute_1.56.0          survival_2.43-3       
##  [34] iterators_1.0.9        gtable_0.2.0           zlibbioc_1.28.0       
##  [37] XVector_0.22.0         kernlab_0.9-27         prabclus_2.2-7        
##  [40] DEoptimR_1.0-8         scales_1.0.0           mvtnorm_1.0-7         
##  [43] DBI_1.0.0              miniUI_0.1.1.1         Rcpp_1.0.1            
##  [46] xtable_1.8-3           progress_1.2.0         htmlTable_1.11.2      
##  [49] mclust_5.4             foreign_0.8-71         bit_1.1-14            
##  [52] preprocessCore_1.44.0  Formula_1.2-3          htmlwidgets_1.3       
##  [55] httr_1.4.0             fpc_2.1-11.1           modeltools_0.2-22     
##  [58] acepack_1.4.1          flexmix_2.3-15         pkgconfig_2.0.2       
##  [61] reshape_0.8.7          XML_3.98-1.11          nnet_7.3-12           
##  [64] locfit_1.5-9.1         labeling_0.3           tidyselect_0.2.5      
##  [67] rlang_0.3.2            later_0.8.0            AnnotationDbi_1.44.0  
##  [70] munsell_0.5.0          cellranger_1.1.0       tools_3.5.2           
##  [73] cli_1.1.0              generics_0.0.2         RSQLite_2.1.1         
##  [76] broom_0.5.1            evaluate_0.13          yaml_2.2.0            
##  [79] knitr_1.22             bit64_0.9-7            robustbase_0.93-0     
##  [82] whisker_0.3-2          mime_0.6               xml2_1.2.0            
##  [85] compiler_3.5.2         rstudioapi_0.7         curl_3.3              
##  [88] affyio_1.52.0          geneplotter_1.60.0     pcaPP_1.9-73          
##  [91] stringi_1.4.3          trimcluster_0.1-2.1    lattice_0.20-38       
##  [94] Matrix_1.2-15          pillar_1.3.1           BiocManager_1.30.4    
##  [97] data.table_1.12.0      bitops_1.0-6           httpuv_1.5.0          
## [100] affy_1.60.0            R6_2.4.0               latticeExtra_0.6-28   
## [103] promises_1.0.1         codetools_0.2-15       MASS_7.3-51.1         
## [106] assertthat_0.2.1       withr_2.1.2            GenomeInfoDbData_1.2.0
## [109] diptest_0.75-7         hms_0.4.2              grid_3.5.2            
## [112] rpart_4.1-13           class_7.3-14           rmarkdown_1.12        
## [115] Cairo_1.5-9            shiny_1.2.0            lubridate_1.7.4       
## [118] base64enc_0.1-3